A method for in-situ monitoring of submarine gas escape based on acoustic-optical-electronic technologies

Abstract

This invention provides a method for in-situ monitoring of seabed gas emissions based on acoustic-optical-electrical technologies. By combining the advantages of acoustic monitoring, fluorescence detection, and natural potential methods, and employing a random forest algorithm for multi-source data fusion and analysis, the accuracy, reliability, and real-time performance of seabed gas emission monitoring are improved. This approach addresses the problems of single-sensor methods in complex marine environments, such as difficulty in obtaining comprehensive information, susceptibility to interference, and insufficient real-time performance, enabling precise and intelligent monitoring of seabed gas emission processes.

Description

Technical Field

[0001] This invention relates to the field of marine environmental monitoring technology, and more specifically, to a method for an in-situ monitoring device for seabed gas escape based on acoustic-optical-electric technology. Background Technology

[0002] There is a lack of mature patented technologies and system solutions for in-situ monitoring of gas escape from the seabed. Existing marine gas monitoring methods mainly focus on monitoring gases in the atmosphere or in shallow sea areas, with relatively little research on monitoring gas escape in deep sea or complex seabed environments.

[0003] Currently, marine gas monitoring primarily employs single-sensor technologies. For example, fiber optic sensors utilize optical fibers to transmit light signals and detect changes in refractive index or light intensity caused by gases. Acoustic methods detect gas escape by analyzing the propagation characteristics of sound waves in seawater. Acoustic methods include active and passive acoustic inversion techniques, mainly utilizing bubble detection, where bubbles act as strong sound sources and scatterers. Electrical sensors utilize the potential difference between different gas and liquid phases to detect the presence and concentration of escaped gases. These are commonly used to monitor gases such as dissolved oxygen, carbon monoxide, and carbon dioxide. These methods are typically used independently, relying on specific types of sensors for data acquisition and analysis.

[0004] Due to the lack of comprehensive monitoring technologies specifically targeting seabed gas escape, existing single-sensor methods have several limitations in practical applications: single sensors are easily affected by environmental factors (such as ocean currents, temperature, and salinity), resulting in significant data noise and making it difficult to accurately identify and quantify gas escape events. Furthermore, relying on only one sensing technology makes it difficult to comprehensively capture the multidimensional information of gas escape, limiting in-depth understanding and analysis of the escape process. Moreover, single-sensor systems have slow response times in complex marine environments, making it difficult to achieve efficient, real-time monitoring and early warning. In terms of data interpretation, existing methods lack effective data fusion techniques, failing to integrate multi-source information to improve overall monitoring performance.

[0005] Therefore, there is an urgent need to develop a seabed gas emission monitoring device and method that can integrate the advantages of multiple sensing technologies and improve monitoring accuracy and reliability through multi-source data fusion, so as to meet the real-time and accurate monitoring needs in complex marine environments. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention provides a method for in-situ monitoring of seabed gas escape based on acoustic-optical-electrical technologies. This technology is widely used in marine scientific research, deep-sea resource exploration, seabed ecological protection, and climate change assessment, aiming to solve the challenges of real-time, accurate, and multi-dimensional information acquisition in monitoring gas escape behavior in complex marine environments. This invention combines the advantages of acoustic monitoring, fluorescence detection, and natural potential methods, and employs a random forest algorithm for multi-source data fusion and analysis, thereby improving the accuracy, reliability, and real-time performance of seabed gas escape monitoring. This approach addresses the problems of single-sensor methods being unable to acquire comprehensive information, being susceptible to interference, and lacking real-time performance in complex marine environments, achieving precise and intelligent monitoring of seabed gas escape processes.

[0007] This invention is achieved through the following technical solution: a method for in-situ monitoring of seabed gas escape based on acoustic-optical-electric technology, wherein the in-situ monitoring device for seabed gas escape includes an acoustic monitoring module, a fluorescence monitoring module, a natural potential probe module, a gimbal support and rotation device.

[0008] The acoustic monitoring module is located at the top of the device and achieves 360° horizontal monitoring through a gimbal support and rotation device. It includes an acoustic monitoring instrument main unit, an acoustic monitoring instrument transducer, and an acoustic monitoring instrument clamping ring. The acoustic monitoring instrument transducer is installed at one end of the acoustic monitoring instrument main unit, and the acoustic monitoring instrument clamping ring is fixedly fitted in the middle of the acoustic monitoring instrument main unit.

[0009] The gimbal support and rotation device includes a gimbal fixing bracket, a gimbal rotation shaft, and a gimbal support device. The top of the gimbal fixing bracket is fixedly connected to the bottom of the acoustic monitoring instrument clamp ring, and the lower end of the gimbal fixing bracket is integrally formed with the gimbal rotation shaft, which is installed inside the gimbal support device.

[0010] The fluorescence monitoring module is installed in the middle of the device and includes several fluorescence monitoring sensors, a fluorescence monitoring body and a fluorescence monitoring conical head. The top of the fluorescence monitoring body is fixedly connected to the gimbal support device. The fluorescence monitoring sensors are evenly distributed on the fluorescence monitoring body and the fluorescence monitoring conical head is fixedly installed at the bottom of the fluorescence monitoring body.

[0011] The natural potential probe module is located at the bottom of the device and includes an electrical monitoring equipment fixing ring and eight flexible electrical probes. The electrical monitoring equipment fixing ring is fixedly fitted to the lower part of the main body of the fluorescence monitor, and the eight flexible electrical probes are equidistantly fixed around the outer wall of the electrical monitoring equipment fixing ring.

[0012] Specifically, the following steps are included:

[0013] Step S1, Data Alignment and Time Synchronization:

[0014] When deploying the device, set the same timestamp format for the acoustic monitoring instrument main unit, the fluorescence monitoring instrument main body, and the flexible electrical probe;

[0015] Step S2, Data Preprocessing and Feature Extraction:

[0016] Step S2-1, Denoising and Filtering: Apply appropriate filtering algorithms to the acoustic signal, fluorescence intensity, and spontaneous potential data to remove high-frequency environmental noise and low-frequency drift; map each source data to [0,1] or standardize it according to its mean and standard deviation;

[0017] Step S2-2: Acoustic signal data features include echo intensity RMS value, volume scattering intensity φ, scattering cross section, and bubble echo frequency band energy; fluorescence intensity data features include reference fluorescence intensity φ0, real-time fluorescence intensity φ, and fluorescence quenching coefficient calculated by the Stern–Volmer equation or after temperature-salt correction; spontaneous potential data features include spontaneous potential variation amplitude ΔE and time change rate; combined features: after aligning the above single-source features by timestamp, a multidimensional feature vector φ(φ) is formed, including acoustic, fluorescence, and electrochemical parameters;

[0018] Step S3, Random Forest Multi-Source Fusion Model:

[0019] Construct the feature vector x(t), x(t) = [I RMS (t),S v (t),I f ′(t),E(t),…],

[0020] Where I RMS Sv represents acoustic characteristics, I f ′ represents the corrected fluorescence intensity, and E is derived from the spontaneous potential measurement; the gas escape intensity is denoted as y(t), which represents the gas volumetric flow rate per unit time or the bubble content per unit volume; in the training set or prior experiments, the recorded data of known gas escape rates are labeled to form {(xi,yi)} training samples.

[0021] During the training phase, B regression trees are trained on B randomly sampled sample sets (Bootstrap sampling). Each tree node randomly selects only a subset of features from all features when splitting. During the prediction phase, new real-time data x(t) is input into each trained decision tree to obtain the regression output value hb(x(t)). Finally, the prediction results of all trees are averaged.

[0022] .

[0023] As a preferred option, the acoustic monitoring instrument host has a built-in signal transmitting and receiving unit, which is responsible for transmitting sound waves and receiving echo signals generated during gas dispersion, and is used to identify the movement trajectory, turbulence characteristics and dispersion intensity of bubbles.

[0024] As a preferred option, several electrodes are arranged on the surface of the flexible electrical probe to monitor the electrical changes in the sediment during the gas escape process in real time by measuring the potential gradient.

[0025] As a preferred approach, the number of trees (B), maximum depth, minimum number of split samples, and number of features extracted (m) are optimized through cross-validation and grid search. Calculations including mean squared error (MSE), mean absolute error (MAE), or coefficient of determination (R²) are used. 2 The index assessment model evaluates the accuracy of its prediction of gas emission intensity.

[0026] By employing the above technical solutions, this invention has the following beneficial effects compared to existing technologies:

[0027] Multi-source data fusion: By integrating acoustic, optical and electrochemical sensor data, it comprehensively captures multi-dimensional information about gas escape, significantly improving the accuracy and reliability of monitoring.

[0028] High-precision monitoring: The random forest algorithm is used to intelligently analyze multi-source data, which can effectively distinguish between gas emission signals and environmental noise, thereby improving monitoring accuracy.

[0029] High real-time performance: The system is designed with real-time data acquisition and processing capabilities, enabling it to respond promptly to gas escape events and provide immediate early warnings.

[0030] Strong anti-interference capability: The integrated application of multiple sensing technologies improves the system's anti-interference capability in complex marine environments and ensures the stability of monitoring data.

[0031] Highly scalable: The system architecture is modularly designed, making it easy to expand and upgrade functions according to actual needs and adapt to different monitoring scenarios.

[0032] Easy to operate: The integrated device design and automated data processing flow simplify the operation steps and lower the barrier to entry.

[0033] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0034] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0035] Figure 1 This is a three-dimensional structural diagram of the present invention;

[0036] Figure 2 This is a schematic diagram of the left-side structure of the present invention;

[0037] Figure 3 This is a schematic diagram of the main structure of the present invention;

[0038] Figure 4 This is a top view of the structure of the present invention;

[0039] Figure 5 A graph of acoustic scattering signal data acquired by acoustic equipment;

[0040] Figure 6 This is a graph showing the change of the potential difference profile over time when measured with a spontaneous potential probe.

[0041] in, Figures 1 to 3 The correspondence between the reference numerals and components in the attached drawings is as follows:

[0042] 1-1: Acoustic Monitoring Instrument Main Unit

[0043] 1-2: Acoustic monitoring instrument transducer

[0044] 1-3: Clamping ring of acoustic monitoring instrument

[0045] 2-1: Fluorescence Monitor Sensor

[0046] 2-2: Main body of the fluorescence monitor

[0047] 2-3: The conical head of the fluorescence monitor

[0048] 3-1: Fixing ring for electrical monitoring equipment

[0049] 3-2: Flexible electrical probe

[0050] 4-1: Gimbal Mounting Bracket

[0051] 4-2: Gimbal Rotation Axis

[0052] 4-3: Gimbal support device. Detailed Implementation

[0053] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0055] The following is combined with Figures 1 to 3 The method of the in-situ monitoring device for seabed gas escape based on acoustic-optical-electric technology according to an embodiment of the present invention will be described in detail.

[0056] like Figures 1 to 4 As shown, this invention proposes a method for in-situ monitoring of seabed gas emissions based on acoustic-optical-electric technology. This method enables efficient monitoring of seabed gas emissions through multi-dimensional means, including acoustic monitoring, fluorescence detection, and spontaneous potential measurement. The in-situ monitoring device for seabed gas emissions includes an acoustic monitoring module, a fluorescence monitoring module, a spontaneous potential probe module, a gimbal support, and a rotation device. The overall structure is shown in the attached figure. Figures 1 to 4 As shown:

[0057] The acoustic monitoring module is located at the top of the device, achieving 360° horizontal monitoring via a pan-tilt support and rotation mechanism. It includes an acoustic monitoring unit 1-1, an acoustic monitoring transducer 1-2, and an acoustic monitoring clamping ring 1-3. The acoustic monitoring transducer 1-2 is mounted at one end of the acoustic monitoring unit 1-1. The acoustic monitoring unit 1-1 has a built-in signal transmitting and receiving unit responsible for transmitting sound waves and receiving echo signals generated during gas dispersion, used to identify the bubble's trajectory, turbulence characteristics, and dispersion intensity. The acoustic monitoring transducer 1-2 converts the sound wave signal into a digital signal and works with the unit to complete signal processing. The acoustic monitoring clamping ring 1-3 is fixedly fitted in the middle of the acoustic monitoring unit 1-1. The clamping ring 1-3 is used to fix the acoustic monitoring unit, ensuring its installation stability, and also supports fine-tuning of angle and position to meet different monitoring needs.

[0058] The gimbal support and rotation device is located at the top of the equipment, providing stable support for the acoustic monitoring module and enabling it to rotate 360°. It includes a gimbal mounting bracket 4-1, a gimbal rotation shaft 4-2, and a gimbal support device 4-3. The top of the gimbal mounting bracket 4-1 is fixedly connected to the bottom of the acoustic monitoring instrument clamp ring 1-3. The lower end of the gimbal mounting bracket 4-1 is integrally formed with the gimbal rotation shaft 4-2, securing the rotating base of the entire device and ensuring overall stability. The gimbal rotation shaft 4-2 provides horizontal rotation capability for the acoustic monitoring module, allowing for full coverage of the acoustic wave detection sector by adjusting the rotation angle. The gimbal rotation shaft 4-2 is installed within the gimbal support device 4-3, which resists environmental interference such as seabed currents, ensuring high precision and stability during operation.

[0059] The fluorescence monitoring module is installed in the middle of the device and is used to detect the fluorescence characteristics of dissolved gases in seawater. Its main components include several fluorescence monitoring sensors 2-1, a fluorescence monitoring body 2-2, and a fluorescence monitoring conical head 2-3. The top of the fluorescence monitoring body 2-2 is fixedly connected to the gimbal support device 4-3. The fluorescence monitoring sensors 2-1 are evenly distributed on the fluorescence monitoring body 2-2. The fluorescence monitoring sensors 2-1 identify changes in the concentration of target gases such as methane through fluorescence signals, providing high-precision chemical information. The fluorescence monitoring body 2-2 includes an excitation light source, optical filters, and a photodetector, used to collect fluorescence signals in real time and convert them into processable electrical signals. The fluorescence monitoring conical head 2-3 is fixedly installed at the bottom of the fluorescence monitoring body 2-2. The fluorescent monitoring conical head 2-3 is used to stabilize and fix the sensors, preventing them from being affected by external disturbances when working on the seabed.

[0060] The spontaneous potential probe module is a crucial unit for monitoring the electrochemical parameters of sediments. Located at the bottom of the device, it comprises an electrical monitoring equipment fixing ring 3-1 and eight flexible electrical probes 3-2. The fixing ring 3-1 is securely mounted on the lower part of the fluorescence monitoring instrument body 2-2. The eight flexible electrical probes 3-2 are equidistantly encircled and fixed to the outer wall of the fixing ring 3-1. The fixing ring 3-1 firmly connects the flexible electrical probes to the overall support device, ensuring measurement stability. Several electrodes are arranged on the surface of the flexible electrical probes 3-2 to monitor the electrical changes in the sediment during gas escape in real time by measuring the potential gradient. The spontaneous potential probe module extends outward in an umbrella-like shape, with eight probes of the same angle and length. This design offers several advantages. First, the evenly distributed probes ensure comprehensive coverage of the monitoring area, reducing blind spots and improving the representativeness and accuracy of the data. Second, the flexible probes provide better stability and reliability, adapting to complex seabed environments and avoiding interference or damage from rigid structures. Furthermore, multiple probes can obtain more comprehensive natural potential data, and data fusion can improve monitoring accuracy and enhance the system's overall effectiveness. Finally, when one probe fails, the others can still provide valid data, thereby improving the system's robustness and reliability and ensuring continuous monitoring.

[0061] The multi-source data fusion method for calculating gas emission intensity specifically includes the following steps:

[0062] Step S1, Data Alignment and Time Synchronization:

[0063] During device deployment, the same timestamp format was used for the acoustic monitoring main unit 1-1, the fluorescence monitoring main unit 2-2, and the flexible electrical probe 3-2 to ensure that subsequent data from each source could be processed under the same time reference frame. The eight flexible electrical probes provided multi-point potential data, accurately reflecting potential differences in different areas of the seabed, which helps in analyzing the patterns of gas escape. Due to their different positions, they also possess data redundancy; even if one probe is interfered with or fails, the others can still provide valid data, ensuring the stability and reliability of the calculation results. Furthermore, the equidistant distribution of the eight probes improved spatial resolution, allowing potential changes to be captured more precisely, providing a more accurate input for calculating gas escape. Through data fusion and algorithm analysis, the combined data from the eight probes further improved the accuracy and reliability of spontaneous potential monitoring.

[0064] Step S2, Data Preprocessing and Feature Extraction:

[0065] Step S2-1, Denoising and Filtering: Apply appropriate filtering algorithms to the acoustic signal, fluorescence intensity, and spontaneous potential data to remove high-frequency environmental noise and low-frequency drift; to eliminate the influence of different dimensions and numerical ranges, each source data can be mapped to [0,1] or standardized according to its mean and standard deviation;

[0066] Step S2-2: Acoustic signal data features include echo intensity RMS value, volume scattering intensity φ, scattering cross section, and bubble echo frequency band energy; fluorescence intensity data features include reference fluorescence intensity φ0, real-time fluorescence intensity φ, and fluorescence quenching coefficient calculated by the Stern–Volmer equation or after temperature-salt correction; spontaneous potential data features include spontaneous potential variation amplitude ΔE and time change rate; combined features: after aligning the above single-source features by timestamp, a multidimensional feature vector φ(φ) is formed, including acoustic, fluorescence, and electrochemical parameters;

[0067] Step S3, Random Forest Multi-Source Fusion Model:

[0068] Construct an feature vector x(t), x(t) = [I RMS(t), Sv(t), If′(t), E(t), ...],

[0069] Where I RMS Sv represents acoustic characteristics, I f ′ represents the corrected fluorescence intensity, and E is derived from the spontaneous potential measurement; the entire fusion model aims to perform regression calculations on the gas emission intensity (or its relative index). The gas emission intensity can be denoted as y(t), representing the gas volumetric flow rate per unit time, or a characterization index of the bubble content per unit volume; in the training set or prior experiments, training samples {(xi,yi)} are formed by labeling the recorded data of known gas emission rates.

[0070] During the training phase, B regression trees are trained on B randomly sampled sample sets (Bootstrap sampling). Each tree node randomly selects only a subset of features from all features when splitting. During the prediction phase, new real-time data x(t) is input into each trained decision tree to obtain the regression output value hb(x(t)). Finally, the prediction results of all trees are averaged.

[0071] .

[0072] The number of trees (B), maximum depth, minimum number of split samples, and number of features extracted (m) are optimized through cross-validation and grid search. The accuracy of the model in predicting gas emission intensity is evaluated by means including mean squared error (MSE), mean absolute error (MAE), or coefficient of determination (R²).

[0073] In the description of this invention, the term "a plurality of" refers to two or more. Unless otherwise explicitly defined, the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. The terms "connection," "installation," "fixing," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0074] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0075] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for in-situ monitoring of seabed gas escape based on acoustic-optical-electric technology, characterized in that... The in-situ monitoring device for seabed gas escape includes an acoustic monitoring module, a fluorescence monitoring module, a natural potential probe module, a gimbal support and rotation device. The acoustic monitoring module is located at the top of the device and achieves 360° horizontal monitoring through a gimbal support and rotation device. It includes an acoustic monitoring instrument host (1-1), an acoustic monitoring instrument transducer (1-2), and an acoustic monitoring instrument clamping ring (1-3). The acoustic monitoring instrument transducer (1-2) is installed at one end of the acoustic monitoring instrument host (1-1), and the acoustic monitoring instrument clamping ring (1-3) is fixedly fitted in the middle of the acoustic monitoring instrument host (1-1). The gimbal support and rotation device includes a gimbal fixing bracket (4-1), a gimbal rotation shaft (4-2), and a gimbal support device (4-3). The top of the gimbal fixing bracket (4-1) is fixedly connected to the bottom of the acoustic monitoring instrument clamp ring (1-3). The lower end of the gimbal fixing bracket (4-1) is integrally formed with the gimbal rotation shaft (4-2). The gimbal rotation shaft (4-2) is installed inside the gimbal support device (4-3). The fluorescence monitoring module is installed in the middle of the device and includes several fluorescence monitoring sensors (2-1), a fluorescence monitoring body (2-2), and a fluorescence monitoring conical head (2-3). The top of the fluorescence monitoring body (2-2) is fixedly connected to the gimbal support device (4-3). The fluorescence monitoring sensors (2-1) are evenly distributed on the fluorescence monitoring body (2-2), and the fluorescence monitoring conical head (2-3) is fixedly installed at the bottom of the fluorescence monitoring body (2-2). The natural potential probe module is located at the bottom of the device and includes an electrical monitoring equipment fixing ring (3-1) and 8 flexible electrical probes (3-2). The electrical monitoring equipment fixing ring (3-1) is fixedly fitted on the lower part of the fluorescence monitor body (2-2), and the 8 flexible electrical probes (3-2) are equidistantly fixed around the outer wall of the electrical monitoring equipment fixing ring (3-1). Specifically, the following steps are included: Step S1, Data Alignment and Time Synchronization: When deploying the device, set the same timestamp format for the acoustic monitoring instrument host (1-1), the fluorescence monitoring instrument body (2-2), and the flexible electrical probe (3-2); Step S2, Data Preprocessing and Feature Extraction: Step S2-1, Denoising and Filtering: Apply appropriate filtering algorithms to the acoustic signal, fluorescence intensity, and spontaneous potential data to remove high-frequency environmental noise and low-frequency drift; map each source data to [0,1] or standardize it according to its mean and standard deviation; Step S2-2: Acoustic signal data features include echo intensity RMS value, volume scattering intensity φ, scattering cross section, and bubble echo frequency band energy; fluorescence intensity data features include reference fluorescence intensity φ0, real-time fluorescence intensity φ, and fluorescence quenching coefficient calculated by the Stern–Volmer equation or after temperature-salt correction; spontaneous potential data features include spontaneous potential variation amplitude ΔE and time change rate; combined features: after aligning the above single-source features by timestamp, a multidimensional feature vector φ(φ) is formed, including acoustic, fluorescence, and electrochemical parameters; Step S3, Random Forest Multi-Source Fusion Model: Construct the feature vector x(t), x(t) = [I RMS (t),S v (t),I f ′(t),E(t),…], Among them I RMS S v For acoustic characteristics, I f ′ represents the corrected fluorescence intensity, and E is derived from the spontaneous potential measurement; the gas escape intensity is denoted as y(t), which represents the gas volumetric flow rate per unit time or the bubble content per unit volume; in the training set or prior experiments, the recorded data of known gas escape rates are labeled to form {(xi,yi)} training samples. During the training phase, B regression trees are trained on B randomly sampled datasets. Each tree node randomly selects a subset of features from all features when splitting. During the prediction phase, new real-time data x(t) is input into each trained decision tree to obtain the regression output value h. b (x(t)), and finally, the prediction results of all trees are averaged: 。 2. The method for an in-situ monitoring device for seabed gas escape based on acoustic-optical-electric technology according to claim 1, characterized in that... The acoustic monitoring instrument host (1-1) has a built-in signal transmission and reception unit, which is responsible for transmitting sound waves and receiving echo signals generated during gas dispersion, and is used to identify the movement trajectory, turbulence characteristics and dispersion intensity of bubbles.

3. The method for an in-situ monitoring device for seabed gas escape based on acoustic-optical-electric technology according to claim 1, characterized in that... The flexible electrical probe (3-2) has several electrodes arranged on its surface. By measuring the potential gradient, the electrical changes in the sediment during the gas escape process can be monitored in real time.

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